APIM - Control - Final Models

knitr::opts_chunk$set(echo = TRUE)

#cmdstanr::set_cmdstan_path(path = "C:/Users/kueng/.cmdstan/cmdstan-2.35.0")
#cmdstanr::set_cmdstan_path(path = "C:/Users/pascku/.cmdstan/cmdstan-2.36.0")

library(tidyverse)
library(R.utils)
library(wbCorr)
library(readxl)
library(kableExtra)
library(brms)
library(bayesplot)
library(see)
library(beepr)
library(DHARMa)
library(digest)



source(file.path('Functions', 'ReportModels.R'))
source(file.path('Functions', 'PrettyTables.R'))
source(file.path('Functions', 'ReportMeasures.R'))
source(file.path('Functions', 'PrepareData.R'))

report_function_hash <- digest::digest(summarize_brms)

system("shutdown /a")

## [1] 1116

# Set options for analysis
use_mi = FALSE
shutdown = FALSE
report_ordinal = FALSE
do_priorsense = FALSE
get_bayesfactor = TRUE
check_models = TRUE # ! set to TRUE !

if (get_bayesfactor) {
  stats_to_report <- c('CI', 'SE', 'pd', 'ROPE', 'BF', 'Rhat', 'ESS')
} else {
  stats_to_report <- c('CI', 'SE', 'pd', 'ROPE', 'Rhat', 'ESS')
}

options(
  dplyr.print_max = 100, 
  brms.backend = 'cmdstan',
  brms.file_refit = ifelse(use_mi, 'never', 'on_change'),
  brms.file_refit = 'on_change',
  #brms.file_refit = 'always',
  error = function() {
    beepr::beep(sound = 5)
    if (shutdown) {
      system("shutdown /s /t 180")
      quit(save = "no", status = 1)
    }
  }
  , es.use_symbols = TRUE
)


####################### Model parameters #######################

iterations = 12000 # 12'000 per chain to achieve 40'000
warmup = 2000 # 2000

# NO AR!!!
#corstr = 'ar'
#corstr = 'cosy_couple'
#corstr = 'cosy_couple:user'


################################################################

suffix = paste0('_final_with_plan_', as.character(iterations))

df <- openxlsx::read.xlsx(file.path('long.xlsx'))
df_original <- df

df_double <- prepare_data(df, recode_pushing = TRUE, use_mi = use_mi)[[1]]

Constructing scales Re-coding pusing reshaping data (4field) centering data within and between

summary(df_double$pushing)

Min. 1st Qu. Median Mean 3rd Qu. Max. NA’s 0.0000 0.0000 0.0000 0.1635 0.0000 5.0000 241

Modelling

# For indistinguishable Dyads
model_rows_fixed <- c(
    'Intercept', 
    # '-- WITHIN PERSON MAIN EFFECTS --', 
    'persuasion_self_cw', 
    'persuasion_partner_cw', 
    'pressure_self_cw', 
    'pressure_partner_cw', 
    'pushing_self_cw', 
    'pushing_partner_cw', 
    'day', 
    'plan_selfPlan',
    'plan_partnerPlan',
    'weartime_self_cw',
    
    # '-- BETWEEN PERSON MAIN EFFECTS',
    'persuasion_self_cb',
    'persuasion_partner_cb',
    'pressure_self_cb',
    'pressure_partner_cb',
    'pushing_self_cb',
    'pushing_partner_cb',
    'weartime_self_cb'
  )


model_rows_fixed_ordinal <- c(
  model_rows_fixed[1],
  'Intercept[1]',
  'Intercept[2]',
  'Intercept[3]',
  'Intercept[4]',
  'Intercept[5]',
  model_rows_fixed[2:length(model_rows_fixed)]
)

model_rows_random <- c(
  # '--------------',
  # '-- RANDOM EFFECTS --',
  'sd(Intercept)', 
  'sd(persuasion_self_cw)',
  'sd(persuasion_partner_cw)',
  'sd(pressure_self_cw)',
  'sd(pressure_partner_cw)',
  'sd(pushing_self_cw)',
  'sd(pushing_partner_cw)',
  # '-- CORRELATION STRUCTURE -- ', 
  'sigma'
)

model_rows_random_ordinal <- c(model_rows_random,'disc')

# For indistinguishable Dyads
model_rownames_fixed <- c(
    "Intercept", 
    # "-- WITHIN PERSON MAIN EFFECTS --", 
    "Daily persuasion experienced", 
    "Daily persuasion utilized (partner's view)", # OR partner received
    "Daily pressure experienced", 
    "Daily pressure utilized (partner's view)", 
    "Daily pushing experienced", 
    "Daily pushing utilized (partner's view)", 
    "Day", 
    "Own Actionplan",
    'Partner Actionplan',
    "Daily weartime",
    
    # "-- BETWEEN PERSON MAIN EFFECTS",
    "Mean persuasion experienced", 
    "Mean persuasion utilized (partner's view)", 
    "Mean pressure experienced", 
    "Mean pressure utilized (partner's view)", 
    "Mean pushing experienced", 
    "Mean pushing utilized (partner's view)", 
    "Mean weartime"
  )


model_rownames_fixed_ordinal <- c(
  model_rownames_fixed[1],
  'Intercept[1]',
  'Intercept[2]',
  'Intercept[3]',
  'Intercept[4]',
  'Intercept[5]',
  model_rownames_fixed[2:length(model_rownames_fixed)]
)

model_rownames_random <- c(
  # '--------------',
  # '-- RANDOM EFFECTS --',
  'sd(Intercept)', 
  "sd(Daily persuasion experienced)", 
  "sd(Daily persuasion utilized (partner's view))", # OR partner received
  "sd(Daily pressure experienced)", 
  "sd(Daily pressure utilized (partner's view))", 
  "sd(Daily pushing experienced)", 
  "sd(Daily pushing utilized (partner's view))", 
  # '-- CORRELATION STRUCTURE -- ', 
  'sigma'
)

model_rownames_random_ordinal <- c(model_rownames_random,'disc')

rows_to_pack <- list(
  "Within-Person Effects" = c(2,11),
  "Between-Person Effects" = c(12,18),
  "Random Effects" = c(19, 25), 
  "Additional Parameters" = c(26,26)
  )


rows_to_pack_ordinal <- list(
  "Within-Person Effects" = c(2+5,11+5),
  "Between-Person Effects" = c(12+5,18+5),
  "Random Effects" = c(19+5, 25+5), 
  "Additional Parameters" = c(26+5,26+6)
  )

Self-Reported MVPA

range(df_double$pa_sub, na.rm = T) 

## [1]   0 720

hist(df_double$pa_sub, breaks = 40) 

hist(log(df_double$pa_sub+00000000001), breaks = 40)

Hurdle Lognormal Model

formula <- bf(
  pa_sub ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    plan_self + plan_partner +
    day + 
    
    # Random effects
    (1 + persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | dd | coupleID),
  
  hu = ~ persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    plan_self + plan_partner +
    day + 
    
    # Random effects
    (1 + persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | dd | coupleID)
  #, autocor = autocor_str
) 

prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b")
  , brms::set_prior("normal(0, 2)", class = "b", dpar = "hu")
  , brms::set_prior("normal(0, 50)", class = "Intercept") # for non-zero PA
  , brms::set_prior("normal(0.5, 2.5)", class = "Intercept", dpar = 'hu') # hurdle part
  , brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
  , brms::set_prior("student_t(3, 0, 2.5)", class = "sigma", lb = 0)
)

#brms::validate_prior(
#  prior1, 
#  formula = formula, 
#  data = df_double, 
#  family = hurdle_lognormal()
#)

#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]

pa_sub <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = brms::hurdle_lognormal(), 
  #family = brms::hurdle_negbinomial(), 
  #family = brms::hurdle_poisson(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = iterations,
  warmup = warmup,
  chains = 4,
  cores = 4,
  seed = 42,
  file = file.path("models_cache_brms", paste0("pa_sub_hu_lognormal", suffix))
  #, file_refit = 'always'
)

## Warning: Rows containing NAs were excluded from the model.

pa_sub_digest <- digest::digest(pa_sub)

if (check_models) {
  check_brms(pa_sub, log_pp_check = TRUE)
  DHARMa.check_brms.all(pa_sub, integer = TRUE, outliers_type = 'bootstrap')
}

## 
## Divergences:

## 0 of 40000 iterations ended with a divergence.

## 
## Tree depth:

## 0 of 40000 iterations saturated the maximum tree depth of 10.

## 
## Energy:

## E-BFMI indicated no pathological behavior.

## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## 
##  DHARMa bootstrapped outlier test
## 
## data:  model.check
## outliers at both margin(s) = 7, observations = 3736, p-value = 0.84
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.0005353319 0.0034930407
## sample estimates:
## outlier frequency (expected: 0.00166488222698073 ) 
##                                        0.001873662

if (do_priorsense) {
  priorsense_vars <- c(
      'Intercept',
      'b_persuasion_self_cw',
      'b_persuasion_partner_cw',
      'b_pressure_self_cw',
      'b_pressure_partner_cw',
      'b_pushing_self_cw',
      'b_pushing_partner_cw'
  )
  
  hurdle_priorsense_vars <- c(
    'Intercept_hu',
    'b_hu_persuasion_self_cw',
    'b_hu_persuasion_partner_cw',
    'b_hu_pressure_self_cw',
    'b_hu_pressure_partner_cw',
    'b_hu_pushing_self_cw',
    'b_hu_pushing_partner_cw'
  )
  
  gc()
  priorsense::powerscale_sensitivity(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
  priorsense::powerscale_plot_dens(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
  priorsense::powerscale_plot_ecdf(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
  priorsense::powerscale_plot_quantities(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
}

# rope range for continuous part of the model
rope_factor <- sd(log(pa_sub$data$pa_sub[pa_sub$data$pa_sub > 0]))
rope_range_continuous = c(-0.1 * rope_factor, 0.1 * rope_factor)

summary_pa_sub <- summarize_brms(
  pa_sub, 
  stats_to_report = stats_to_report,
  rope_range = rope_range_continuous,
  hu_rope_range = c(-0.18, 0.18),
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) 

## Sampling priors, please wait...

## Warning in summarize_brms(pa_sub, stats_to_report = stats_to_report, rope_range
## = rope_range_continuous, : Coefficients were exponentiated. Double check if
## this was intended.

# Print the updated dataframe
summary_pa_sub %>%
  print_df(rows_to_pack = rows_to_pack)

	exp(Est.)_hu	SE_hu	95% CI_hu	pd_hu	ROPE_hu	inside ROPE_hu	BF_hu	BF_Evidence_hu	Rhat_hu	Bulk_ESS_hu	Tail_ESS_hu	exp(Est.)_nonzero	SE_nonzero	95% CI_nonzero	pd_nonzero	ROPE_nonzero	inside ROPE_nonzero	BF_nonzero	BF_Evidence_nonzero	Rhat_nonzero	Bulk_ESS_nonzero	Tail_ESS_nonzero
Intercept	0.26***	0.04	[ 0.19, 0.35]	1.000	[0.84, 1.20]	0.000	>100	Overwhelming Evidence	1.000	16190	23866	37.81***	2.61	[32.93, 43.41]	1.000	[0.92, 1.08]	0.000	>100	Overwhelming Evidence	1.000	9116	17782
Within-Person Effects
Daily persuasion experienced	1.58***	0.11	[ 1.39, 1.83]	1.000	[0.84, 1.20]	0.000	>100	Overwhelming Evidence	1.000	23006	23930	1.03	0.03	[ 0.98, 1.09]	0.864	[0.92, 1.08]	0.965	0.019	Very Strong Evidence for Null	1.000	12549	20526
Daily persuasion utilized (partner’s view)	1.34***	0.08	[ 1.19, 1.52]	1.000	[0.84, 1.20]	0.036	>100	Overwhelming Evidence	1.000	23811	23955	1.03	0.02	[ 0.99, 1.08]	0.928	[0.92, 1.08]	0.974	0.027	Very Strong Evidence for Null	1.000	16807	24611
Daily pressure experienced	0.96	0.15	[ 0.70, 1.31]	0.610	[0.84, 1.20]	0.733	0.079	Strong Evidence for Null	1.000	26140	24643	0.91	0.05	[ 0.82, 1.00]	0.973	[0.92, 1.08]	0.346	0.153	Moderate Evidence for Null	1.000	25200	24082
Daily pressure utilized (partner’s view)	1.49*	0.28	[ 1.04, 2.33]	0.985	[0.84, 1.20]	0.115	1.026	Weak Evidence	1.000	26390	19117	0.95	0.04	[ 0.86, 1.03]	0.892	[0.92, 1.08]	0.699	0.037	Strong Evidence for Null	1.000	25496	25293
Daily pushing experienced	0.95	0.14	[ 0.71, 1.31]	0.644	[0.84, 1.20]	0.733	0.083	Strong Evidence for Null	1.000	18845	22848	0.99	0.03	[ 0.92, 1.05]	0.636	[0.92, 1.08]	0.970	0.014	Very Strong Evidence for Null	1.000	21517	25787
Daily pushing utilized (partner’s view)	1.29*	0.14	[ 1.05, 1.63]	0.992	[0.84, 1.20]	0.247	1.084	Weak Evidence	1.000	28218	23031	0.96	0.03	[ 0.90, 1.02]	0.915	[0.92, 1.08]	0.878	0.031	Strong Evidence for Null	1.000	22935	25188
Day	0.86	0.13	[ 0.65, 1.15]	0.839	[0.84, 1.20]	0.579	0.121	Moderate Evidence for Null	1.000	42354	30685	0.99	0.06	[ 0.87, 1.11]	0.576	[0.92, 1.08]	0.790	0.025	Very Strong Evidence for Null	1.000	34268	29771
Own Actionplan	9.46***	0.95	[ 7.77, 11.57]	1.000	[0.84, 1.20]	0.000	>100	Overwhelming Evidence	1.000	33658	29366	1.32***	0.06	[ 1.21, 1.45]	1.000	[0.92, 1.08]	0.000	>100	Overwhelming Evidence	1.000	32021	29591
Partner Actionplan	1.17	0.12	[ 0.96, 1.41]	0.939	[0.84, 1.20]	0.598	0.164	Moderate Evidence for Null	1.000	34689	30074	1.08	0.05	[ 0.99, 1.17]	0.956	[0.92, 1.08]	0.547	0.074	Strong Evidence for Null	1.000	32664	30207
Daily weartime	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Between-Person Effects
Mean persuasion experienced	1.48	0.53	[ 0.73, 3.06]	0.864	[0.84, 1.20]	0.218	0.326	Weak Evidence for Null	1.000	10056	17665	1.00	0.15	[ 0.74, 1.34]	0.513	[0.92, 1.08]	0.402	0.060	Strong Evidence for Null	1.001	5633	11454
Mean persuasion utilized (partner’s view)	1.40	0.50	[ 0.69, 2.86]	0.827	[0.84, 1.20]	0.257	0.280	Moderate Evidence for Null	1.000	10122	17569	0.95	0.14	[ 0.71, 1.28]	0.636	[0.92, 1.08]	0.379	0.063	Strong Evidence for Null	1.001	5743	11411
Mean pressure experienced	0.39*	0.16	[ 0.17, 0.88]	0.988	[0.84, 1.20]	0.030	2.751	Weak Evidence	1.000	14873	22414	1.20	0.21	[ 0.85, 1.70]	0.859	[0.92, 1.08]	0.202	0.123	Moderate Evidence for Null	1.000	9138	17519
Mean pressure utilized (partner’s view)	0.49	0.21	[ 0.22, 1.13]	0.952	[0.84, 1.20]	0.088	0.862	Weak Evidence for Null	1.000	15923	23285	0.94	0.17	[ 0.66, 1.34]	0.638	[0.92, 1.08]	0.315	0.077	Strong Evidence for Null	1.000	9848	18722
Mean pushing experienced	1.09	0.58	[ 0.38, 3.17]	0.566	[0.84, 1.20]	0.261	0.274	Moderate Evidence for Null	1.000	14479	22250	1.23	0.27	[ 0.80, 1.92]	0.831	[0.92, 1.08]	0.177	0.139	Moderate Evidence for Null	1.000	8663	15473
Mean pushing utilized (partner’s view)	1.96	1.04	[ 0.68, 5.67]	0.896	[0.84, 1.20]	0.122	0.589	Weak Evidence for Null	1.000	14739	22769	1.34	0.30	[ 0.86, 2.09]	0.900	[0.92, 1.08]	0.122	0.206	Moderate Evidence for Null	1.000	8551	15339
Mean weartime	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Random Effects
sd(Intercept)	0.74	0.11	[0.57, 0.99]	NA	NA	NA	NA	NA	1.000	15110	21952	0.30	0.04	[0.23, 0.40]	NA	NA	NA	NA	NA	1.000	8864	14501
sd(Daily persuasion experienced)	0.21	0.09	[0.03, 0.40]	NA	NA	NA	NA	NA	1.000	10199	7978	0.11	0.02	[0.07, 0.17]	NA	NA	NA	NA	NA	1.000	16844	22338
sd(Daily persuasion utilized (partner’s view))	0.15	0.09	[0.01, 0.34]	NA	NA	NA	NA	NA	1.000	10033	11831	0.08	0.02	[0.04, 0.13]	NA	NA	NA	NA	NA	1.000	16476	14601
sd(Daily pressure experienced)	0.19	0.18	[0.01, 0.74]	NA	NA	NA	NA	NA	1.000	16018	15844	0.07	0.06	[0.00, 0.23]	NA	NA	NA	NA	NA	1.001	11532	14504
sd(Daily pressure utilized (partner’s view))	0.24	0.22	[0.01, 0.95]	NA	NA	NA	NA	NA	1.000	12672	15127	0.06	0.05	[0.00, 0.18]	NA	NA	NA	NA	NA	1.000	15058	13413
sd(Daily pushing experienced)	0.58	0.17	[0.31, 1.01]	NA	NA	NA	NA	NA	1.000	16621	21384	0.09	0.04	[0.01, 0.17]	NA	NA	NA	NA	NA	1.000	9410	7939
sd(Daily pushing utilized (partner’s view))	0.18	0.14	[0.01, 0.50]	NA	NA	NA	NA	NA	1.000	13124	13776	0.07	0.04	[0.01, 0.15]	NA	NA	NA	NA	NA	1.001	10096	7561
Additional Parameters
sigma	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.68	0.01	[0.65, 0.70]	NA	NA	NA	NA	NA	1.000	35463	30410

# Plot continuous part of model

variable <- c(
  '(Intercept)',
  'b_persuasion_self_cw',
  'b_persuasion_partner_cw',
  'b_pressure_self_cw',
  'b_pressure_partner_cw',
  'b_pushing_self_cw',
  'b_pushing_partner_cw'
)


plot(
  bayestestR::p_direction(pa_sub, parameter = variable),
  priors = TRUE
) + theme_bw()

## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length

## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length

plot(
  bayestestR::rope(
    pa_sub, 
    parameter = variable, 
    range = rope_range_continuous,
    verbose = F,
    ci = 1
  )
) + theme_bw()

# Hurdle part of the model
variable <- c(
  'b_hu_persuasion_self_cw',
  'b_hu_persuasion_partner_cw',
  'b_hu_pressure_self_cw',
  'b_hu_pressure_partner_cw',
  'b_hu_pushing_self_cw',
  'b_hu_pushing_partner_cw'
)

plot(
  bayestestR::p_direction(pa_sub, parameter = variable),
  priors = TRUE
) + theme_bw()

## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length

# The rope range for the bernoulli part of the model is -0.18, 0.18
plot(
  bayestestR::rope(pa_sub, parameter = variable, range = c(-0.18, 0.18), ci = 1),
  verbose = FALSE
) + theme_bw()

## Possible multicollinearity between b_persuasion_partner_cb and
##   b_persuasion_self_cb (r = 0.75), b_hu_persuasion_partner_cb and
##   b_hu_persuasion_self_cb (r = 0.75). This might lead to inappropriate
##   results. See 'Details' in '?rope'.

Hurdle part of the model on the left, non-zero part towards the right side of the table

conds_eff <- conditional_spaghetti(
  pa_sub, 
  effects = c(
    'persuasion_self_cw',
    'persuasion_partner_cw',
    'pressure_self_cw',
    'pressure_partner_cw',
    'pushing_self_cw',
    'pushing_partner_cw'
  ),
  x_label = c(
    'Received Persuasion',
    'Exerted Persuasion',
    'Received Pressure',
    'Exerted Pressure',
    'Received Plan-Related Pushing',
    'Exerted Plan-Related Pushing'
  ),
  group_var = 'coupleID',
  plot_full_range = TRUE,
  y_limits = c(0, 100),
  y_label = "Same-Day MVPA",
  y_labels = c('Probability of Being Active', 'Minutes of MVPA When Active', 'Overall Expected Minutes of MVPA'),
  , filter_quantiles = .9995
  , font_family = 'Candara'
)

## This is posterior version 1.6.0

## 
## Attaching package: 'posterior'

## The following object is masked from 'package:bayesplot':
## 
##     rhat

## The following objects are masked from 'package:stats':
## 
##     mad, sd, var

## The following objects are masked from 'package:base':
## 
##     %in%, match

## Registering fonts with R

## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.

print(conds_eff)

$persuasion_self_cw

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database

## Warning: Removed 73 rows containing missing values or values outside the scale range
## (`geom_line()`).

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning: Removed 26 rows containing missing values or values outside the scale range
## (`geom_line()`).

## Picking joint bandwidth of 0.00805

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

$persuasion_partner_cw

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning: Removed 50 rows containing missing values or values outside the scale range
## (`geom_line()`).

## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_line()`).

## Picking joint bandwidth of 0.00634

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

$pressure_self_cw

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_line()`).

## Picking joint bandwidth of 0.0113

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

$pressure_partner_cw

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Picking joint bandwidth of 0.0186

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

$pushing_self_cw

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Picking joint bandwidth of 0.0106

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
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## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
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$pushing_partner_cw

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Picking joint bandwidth of 0.00978

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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Note. This graphic illustrates the relationship between social control and moderate to vigorous physical activity (MVPA) using a Bayesian Hurdle-Lognormal Multilevel Model. The predictor is centered within individuals to examine how deviations from their average social control relate to same-day MVPA. Shaded areas indicate credible intervals, thick lines show fixed effects, and thin lines represent random effects, highlighting variability across couples. The plots display the probability of being active, expected minutes of MVPA when active, and combined predicted MVPA. The bottom density plot visualizes the posterior distributions of slope estimates, transformed to represent multiplicative changes in odds ratios (hurdle component) or expected values. Medians and 95% credible intervals (2.5th and 97.5th percentiles) are shown. Effects are significant, when the 95% credible interval does not overlap 1.

x_label = c(
    'Received Persuasion',
    'Exerted Persuasion',
    'Received Pressure',
    'Exerted Pressure',
    'Received Plan-Related Pushing',
    'Exerted Plan-Related Pushing'
  )

home_dir <- getwd()
output_dir <- file.path(home_dir, 'Output', 'Plots')

for (i in 1:length(conds_eff)) {
  effname <- names(conds_eff)[i]
  eff_plot <- conds_eff[[i]]
  x_label_i <- x_label[[i]]
  
  rmarkdown::render(
    file.path(output_dir, 'BeautifulPlotWithNote.Rmd'), 
    output_file = file.path(output_dir, paste0('Graphic_', effname, '.pdf')),
    params = list(
      home_dir = home_dir,
      output_dir = output_dir,
      p_i = eff_plot,
      p_name = effname,
      x_label = x_label_i
      ),
    envir = new.env(),
    quiet = TRUE
  )
}

print('done')

Comparing effect size of pressure and pushing

hypothesis(pa_sub, "pressure_self_cw < pushing_self_cw")

## Hypothesis Tests for class b:
##                 Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (pressure_self_cw... < 0    -0.09      0.07    -0.19     0.02      10.45
##   Post.Prob Star
## 1      0.91     
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.

Device Based MVPA

range(df_double$pa_obj, na.rm = T) 

## [1]   5.75 971.25

hist(df_double$pa_obj, breaks = 50)

df_double$pa_obj_log <- log(df_double$pa_obj)

hist(df_double$pa_obj_log, breaks = 50)

Lognormal Model

formula <- bf(
  pa_obj ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    plan_self + plan_partner +
    day + weartime_self_cw + weartime_self_cb +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID)
  #, autocor = autocor_str
)



prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b")
  , brms::set_prior("normal(0, 50)", class = "Intercept") 
  , brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
  , brms::set_prior("student_t(3, 0, 2.5)", class = "sigma", lb = 0)
)


#brms::validate_prior(
#  prior1, 
#  formula = formula, 
#  data = df_double, 
#  family = lognormal()
#  )

#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]

pa_obj_log <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = lognormal(),
  #control = list(adapt_delta = 0.95),
  iter = iterations,
  warmup = warmup,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path("models_cache_brms", paste0("pa_obj_log_gaussian", suffix))
)

## Warning: Rows containing NAs were excluded from the model.

pa_obj_log_digest <- digest::digest(pa_obj_log)

if (check_models) {
  check_brms(pa_obj_log, log_pp_check = TRUE)
  DHARMa.check_brms.all(pa_obj_log, integer = TRUE, outliers_type = 'bootstrap')
}

## 
## Divergences:

## 0 of 40000 iterations ended with a divergence.

## 
## Tree depth:

## 0 of 40000 iterations saturated the maximum tree depth of 10.

## 
## Energy:

## E-BFMI indicated no pathological behavior.

## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## DHARMa:testOutliers with type = binomial may have inflated Type I error rates for integer-valued distributions. To get a more exact result, it is recommended to re-run testOutliers with type = 'bootstrap'. See ?testOutliers for details

## 
##  DHARMa bootstrapped outlier test
## 
## data:  model.check
## outliers at both margin(s) = 23, observations = 3337, p-value < 2.2e-16
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.001341025 0.005394067
## sample estimates:
## outlier frequency (expected: 0.00311057836379982 ) 
##                                        0.006892418

if (do_priorsense) {
  gc()
  priorsense::powerscale_sensitivity(pa_obj_log, variable = priorsense_vars)
  priorsense::powerscale_plot_dens(pa_obj_log, variable = priorsense_vars)
  priorsense::powerscale_plot_ecdf(pa_obj_log, variable = priorsense_vars)
  priorsense::powerscale_plot_quantities(pa_obj_log, variable = priorsense_vars)
}

# rope range for lognormal model
rope_factor <- sd(log(pa_obj_log$data$pa_obj))
rope_range_log = c(-0.1 * rope_factor, 0.1 * rope_factor)

summarize_brms(
  pa_obj_log, 
  stats_to_report = stats_to_report,
  rope_range = rope_range_log,
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) %>%
  print_df(rows_to_pack = rows_to_pack)

## Sampling priors, please wait...

## Warning in summarize_brms(pa_obj_log, stats_to_report = stats_to_report, :
## Coefficients were exponentiated. Double check if this was intended.

	exp(Est.)	SE	95% CI	pd	ROPE	inside ROPE	BF	BF_Evidence	Rhat	Bulk_ESS	Tail_ESS
Intercept	111.17***	5.99	[99.96, 123.69]	1.000	[0.94, 1.07]	0.000	>100	Overwhelming Evidence	1.001	6853	13721
Within-Person Effects
Daily persuasion experienced	1.03	0.02	[ 1.00, 1.06]	0.955	[0.94, 1.07]	0.995	0.026	Very Strong Evidence for Null	1.000	26837	29788
Daily persuasion utilized (partner’s view)	1.02	0.02	[ 0.99, 1.05]	0.869	[0.94, 1.07]	0.998	0.012	Very Strong Evidence for Null	1.000	30744	30830
Daily pressure experienced	0.95	0.03	[ 0.88, 1.01]	0.949	[0.94, 1.07]	0.607	0.055	Strong Evidence for Null	1.000	45516	30355
Daily pressure utilized (partner’s view)	0.98	0.03	[ 0.92, 1.05]	0.693	[0.94, 1.07]	0.914	0.015	Very Strong Evidence for Null	1.000	45462	30752
Daily pushing experienced	1.01	0.02	[ 0.96, 1.07]	0.724	[0.94, 1.07]	0.976	0.012	Very Strong Evidence for Null	1.000	34837	29973
Daily pushing utilized (partner’s view)	1.00	0.02	[ 0.96, 1.04]	0.515	[0.94, 1.07]	0.997	0.008	Very Strong Evidence for Null	1.000	48804	32202
Day	0.97	0.03	[ 0.91, 1.04]	0.788	[0.94, 1.07]	0.845	0.019	Very Strong Evidence for Null	1.000	68958	29888
Own Actionplan	1.06*	0.03	[ 1.01, 1.12]	0.994	[0.94, 1.07]	0.530	0.238	Moderate Evidence for Null	1.000	55369	33013
Partner Actionplan	1.05	0.03	[ 1.00, 1.10]	0.971	[0.94, 1.07]	0.754	0.061	Strong Evidence for Null	1.000	54436	32418
Daily weartime	1.00***	0.00	[ 1.00, 1.00]	1.000	[0.94, 1.07]	1.000	0.054	Strong Evidence for Null	1.000	40661	25023
Between-Person Effects
Mean persuasion experienced	1.10	0.15	[ 0.84, 1.46]	0.763	[0.94, 1.07]	0.279	0.071	Strong Evidence for Null	1.001	5519	10526
Mean persuasion utilized (partner’s view)	0.98	0.14	[ 0.74, 1.30]	0.560	[0.94, 1.07]	0.350	0.057	Strong Evidence for Null	1.001	5433	10637
Mean pressure experienced	0.99	0.14	[ 0.74, 1.32]	0.529	[0.94, 1.07]	0.347	0.058	Strong Evidence for Null	1.001	7724	15176
Mean pressure utilized (partner’s view)	0.97	0.14	[ 0.74, 1.28]	0.580	[0.94, 1.07]	0.348	0.057	Strong Evidence for Null	1.001	7235	14417
Mean pushing experienced	0.95	0.19	[ 0.63, 1.42]	0.605	[0.94, 1.07]	0.242	0.084	Strong Evidence for Null	1.000	7861	14606
Mean pushing utilized (partner’s view)	1.22	0.24	[ 0.82, 1.83]	0.836	[0.94, 1.07]	0.155	0.129	Moderate Evidence for Null	1.000	7743	14434
Mean weartime	1.00	0.00	[ 1.00, 1.00]	0.920	[0.94, 1.07]	1.000	0.000	Very Strong Evidence for Null	1.000	53874	35355
Random Effects
sd(Intercept)	0.29	0.04	[0.23, 0.39]	NA	NA	NA	NA	NA	1.000	8837	15994
sd(Daily persuasion experienced)	0.05	0.01	[0.02, 0.08]	NA	NA	NA	NA	NA	1.000	15960	11070
sd(Daily persuasion utilized (partner’s view))	0.05	0.02	[0.03, 0.09]	NA	NA	NA	NA	NA	1.001	14326	11209
sd(Daily pressure experienced)	0.04	0.03	[0.00, 0.13]	NA	NA	NA	NA	NA	1.000	19563	20365
sd(Daily pressure utilized (partner’s view))	0.03	0.03	[0.00, 0.11]	NA	NA	NA	NA	NA	1.000	23646	19725
sd(Daily pushing experienced)	0.07	0.04	[0.01, 0.15]	NA	NA	NA	NA	NA	1.000	9254	11567
sd(Daily pushing utilized (partner’s view))	0.03	0.03	[0.00, 0.09]	NA	NA	NA	NA	NA	1.000	15768	18355
Additional Parameters
sigma	0.57	0.01	[0.56, 0.59]	NA	NA	NA	NA	NA	1.000	66437	30244

plot(
  bayestestR::p_direction(pa_obj_log),
  priors = TRUE
) + 
  coord_cartesian(xlim = c(-3, 3)) +
  theme_bw()

## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length

## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length

plot(
  bayestestR::rope(pa_obj_log, range = rope_range_log, ci = 1)
) + theme_bw()

## Possible multicollinearity between b_persuasion_partner_cb and
##   b_persuasion_self_cb (r = 0.89), b_pressure_self_cb and
##   b_persuasion_self_cb (r = 0.73), b_pressure_partner_cb and
##   b_persuasion_self_cb (r = 0.73), b_pressure_self_cb and
##   b_persuasion_partner_cb (r = 0.71), b_pressure_partner_cb and
##   b_persuasion_partner_cb (r = 0.77), b_pushing_partner_cb and
##   b_pushing_self_cb (r = 0.83). This might lead to inappropriate results.
##   See 'Details' in '?rope'.

# Nothing significant, no plots

Affect

range(df_double$aff, na.rm = T)

## [1] 0 5

hist(df_double$aff, breaks = 15)

Gaussian

formula <- bf(
  aff ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    plan_self + plan_partner +
    day +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID)
  #, autocor = autocor_str
)


prior1 <- c(
  brms::set_prior("normal(0, 5)", class = "b")
  ,brms::set_prior("normal(0, 20)", class = "Intercept", lb=1, ub=6)
  , brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
  , brms::set_prior("student_t(3, 0, 2.5)", class = "sigma", lb = 0)
)

#brms::validate_prior(
#  prior1, 
#  formula = formula, 
#  data = df_double, 
#  family = gaussian()
#  )

#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]

mood_gauss <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = gaussian(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = iterations,
  warmup = warmup,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path("models_cache_brms", paste0("mood_gauss", suffix))
)

## Warning: Rows containing NAs were excluded from the model.

mood_gauss_digest <- digest::digest(mood_gauss)

if (check_models) {
  check_brms(mood_gauss, log_pp_check = FALSE)
  DHARMa.check_brms.all(mood_gauss, integer = FALSE)
}

## 
## Divergences:

## 0 of 40000 iterations ended with a divergence.

## 
## Tree depth:

## 0 of 40000 iterations saturated the maximum tree depth of 10.

## 
## Energy:

## E-BFMI indicated no pathological behavior.

## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## 
##  DHARMa outlier test based on exact binomial test with approximate
##  expectations
## 
## data:  model.check
## outliers at both margin(s) = 32, observations = 3736, p-value =
## 2.24e-11
## alternative hypothesis: true probability of success is not equal to 0.001998002
## 95 percent confidence interval:
##  0.005865832 0.012070325
## sample estimates:
## frequency of outliers (expected: 0.001998001998002 ) 
##                                           0.00856531

if (do_priorsense) {
  gc()
  priorsense::powerscale_sensitivity(mood_gauss, variable = priorsense_vars)
  priorsense::powerscale_plot_dens(mood_gauss, variable = priorsense_vars)
  priorsense::powerscale_plot_ecdf(mood_gauss, variable = priorsense_vars)
  priorsense::powerscale_plot_quantities(mood_gauss, variable = priorsense_vars)
}

summarize_brms(
  mood_gauss, 
  stats_to_report = stats_to_report,
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = F) %>%
  print_df(rows_to_pack = rows_to_pack)

## Warning in compute_rope(range = rope_range): Collinearity detected. Some VIFs
## are > 10. This may invalidate ROPE inferences!

Check for Multicollinearity

Low Correlation

              Term  VIF     VIF 95% CI Increased SE Tolerance
persuasion_self_cw 1.12 [ 1.09,  1.16]         1.06      0.89

persuasion_partner_cw 1.08 [ 1.05, 1.12] 1.04 0.92 pressure_self_cw 1.09 [ 1.06, 1.13] 1.04 0.92 pressure_partner_cw 1.08 [ 1.05, 1.12] 1.04 0.93 pushing_self_cw 1.16 [ 1.13, 1.21] 1.08 0.86 pushing_partner_cw 1.15 [ 1.12, 1.20] 1.07 0.87 plan_self 1.28 [ 1.24, 1.33] 1.13 0.78 plan_partner 1.27 [ 1.23, 1.32] 1.13 0.79 day 1.05 [ 1.02, 1.09] 1.02 0.95 Tolerance 95% CI [0.86, 0.92] [0.89, 0.95] [0.88, 0.94] [0.89, 0.95] [0.83, 0.89] [0.84, 0.89] [0.75, 0.81] [0.76, 0.82] [0.91, 0.98]

Moderate Correlation

            Term  VIF     VIF 95% CI Increased SE Tolerance
pressure_self_cb 5.72 [ 5.43,  6.04]         2.39      0.17

pressure_partner_cb 5.70 [ 5.40, 6.02] 2.39 0.18 pushing_self_cb 8.59 [ 8.13, 9.08] 2.93 0.12 pushing_partner_cb 8.59 [ 8.13, 9.08] 2.93 0.12 Tolerance 95% CI [0.17, 0.18] [0.17, 0.19] [0.11, 0.12] [0.11, 0.12]

High Correlation

              Term   VIF     VIF 95% CI Increased SE Tolerance
persuasion_self_cb 14.30 [13.51, 15.14]         3.78      0.07

persuasion_partner_cb 14.23 [13.45, 15.06] 3.77 0.07 Tolerance 95% CI [0.07, 0.07] [0.07, 0.07]

## Sampling priors, please wait...

	Est.	SE	95% CI	pd	ROPE	inside ROPE	BF	BF_Evidence	Rhat	Bulk_ESS	Tail_ESS
Intercept	3.67***	0.10	[ 3.46, 3.88]	1.000	[-0.11, 0.11]	0.000	>100	Overwhelming Evidence	1.000	4848	10520
Within-Person Effects
Daily persuasion experienced	0.00	0.02	[-0.04, 0.04]	0.526	[-0.11, 0.11]	1.000	0.004	Very Strong Evidence for Null	1.000	40632	30166
Daily persuasion utilized (partner’s view)	0.02	0.02	[-0.02, 0.07]	0.812	[-0.11, 0.11]	1.000	0.007	Very Strong Evidence for Null	1.000	33574	28981
Daily pressure experienced	-0.03	0.05	[-0.14, 0.07]	0.715	[-0.11, 0.11]	0.937	0.012	Very Strong Evidence for Null	1.000	42308	29745
Daily pressure utilized (partner’s view)	-0.03	0.06	[-0.15, 0.08]	0.692	[-0.11, 0.11]	0.926	0.012	Very Strong Evidence for Null	1.000	36022	25888
Daily pushing experienced	0.01	0.03	[-0.06, 0.07]	0.575	[-0.11, 0.11]	0.999	0.007	Very Strong Evidence for Null	1.000	42341	29601
Daily pushing utilized (partner’s view)	0.07*	0.03	[ 0.00, 0.14]	0.976	[-0.11, 0.11]	0.895	0.058	Strong Evidence for Null	1.000	32280	28722
Day	0.26***	0.06	[ 0.15, 0.37]	1.000	[-0.11, 0.11]	0.005	>100	Overwhelming Evidence	1.000	61272	31008
Own Actionplan	0.10**	0.04	[ 0.03, 0.18]	0.996	[-0.11, 0.11]	0.617	0.271	Moderate Evidence for Null	1.000	56031	33570
Partner Actionplan	-0.03	0.04	[-0.11, 0.04]	0.794	[-0.11, 0.11]	0.984	0.011	Very Strong Evidence for Null	1.000	55468	31136
Daily weartime	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Between-Person Effects
Mean persuasion experienced	0.34	0.27	[-0.21, 0.89]	0.894	[-0.11, 0.11]	0.153	0.121	Moderate Evidence for Null	1.000	4696	8471
Mean persuasion utilized (partner’s view)	0.23	0.27	[-0.33, 0.77]	0.795	[-0.11, 0.11]	0.235	0.078	Strong Evidence for Null	1.000	4671	8336
Mean pressure experienced	-0.30	0.27	[-0.84, 0.24]	0.864	[-0.11, 0.11]	0.183	0.102	Moderate Evidence for Null	1.000	5722	10724
Mean pressure utilized (partner’s view)	-0.31	0.27	[-0.85, 0.23]	0.878	[-0.11, 0.11]	0.171	0.106	Moderate Evidence for Null	1.000	5707	11362
Mean pushing experienced	0.19	0.38	[-0.58, 0.97]	0.690	[-0.11, 0.11]	0.207	0.086	Strong Evidence for Null	1.000	6682	12677
Mean pushing utilized (partner’s view)	0.35	0.38	[-0.40, 1.12]	0.825	[-0.11, 0.11]	0.154	0.118	Moderate Evidence for Null	1.000	6768	12627
Mean weartime	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Random Effects
sd(Intercept)	0.60	0.07	[0.47, 0.78]	NA	NA	NA	NA	NA	1.000	7243	13340
sd(Daily persuasion experienced)	0.04	0.03	[0.00, 0.10]	NA	NA	NA	NA	NA	1.000	11314	16271
sd(Daily persuasion utilized (partner’s view))	0.07	0.03	[0.01, 0.13]	NA	NA	NA	NA	NA	1.000	9554	7536
sd(Daily pressure experienced)	0.07	0.06	[0.00, 0.23]	NA	NA	NA	NA	NA	1.000	17500	19124
sd(Daily pressure utilized (partner’s view))	0.08	0.07	[0.00, 0.26]	NA	NA	NA	NA	NA	1.000	15715	19155
sd(Daily pushing experienced)	0.05	0.04	[0.00, 0.14]	NA	NA	NA	NA	NA	1.000	15547	17454
sd(Daily pushing utilized (partner’s view))	0.07	0.05	[0.00, 0.16]	NA	NA	NA	NA	NA	1.000	13051	13306
Additional Parameters
sigma	0.96	0.01	[0.94, 0.98]	NA	NA	NA	NA	NA	1.000	66577	30744

plot(
  bayestestR::p_direction(mood_gauss),
  priors = TRUE
)  + 
  coord_cartesian(xlim = c(-3, 3)) +
  theme_bw()

## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length

## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length

plot(
  bayestestR::rope(mood_gauss, ci = 1)
) + theme_bw()

## Possible multicollinearity between b_pressure_self_cb and
##   b_persuasion_self_cb (r = 0.82), b_pressure_partner_cb and
##   b_persuasion_self_cb (r = 0.8), b_pressure_self_cb and
##   b_persuasion_partner_cb (r = 0.8), b_pressure_partner_cb and
##   b_persuasion_partner_cb (r = 0.82), b_pressure_partner_cb and
##   b_pressure_self_cb (r = 0.76), b_pushing_partner_cb and
##   b_pushing_self_cb (r = 0.89). This might lead to inappropriate results.
##   See 'Details' in '?rope'.

conditional_spaghetti(
  mood_gauss, 
  effects = c('pushing_partner_cw'),
  group_var = 'coupleID',
  plot_full_range = TRUE
)

$pushing_partner_cw

Reactance

range(df_double$reactance, na.rm = T) 

## [1] 0 5

hist(df_double$reactance, breaks = 7) 

hist(log(df_double$reactance+0.1), breaks = 10)

Ordinal

df_double$reactance_ordinal <- factor(df_double$reactance,
                                      levels = 0:5, 
                                      ordered = TRUE)

formula <- bf(
  reactance_ordinal ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    plan_self + plan_partner +
    day +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID)
  #, autocor = autocor_str
)


prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b")
  , brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
)


#brms::validate_prior(
#  prior1, 
#  formula = formula, 
#  data = df_double, 
#  family = cumulative() # HURDLE_CUMULATIVE
#  )


#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]

reactance_ordinal <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = brms::cumulative(),
  #control = list(adapt_delta = 0.95),
  iter = iterations,
  warmup = warmup,
  chains = 4,
  cores = 4,
  seed = 7777
  , file = file.path("models_cache_brms", paste0("reactance_ordinal", suffix))
)

## Warning: Rows containing NAs were excluded from the model.

reactance_ordinal_digest <- digest::digest(reactance_ordinal)

if (check_models) {
  check_brms(reactance_ordinal)
  DHARMa.check_brms.all(reactance_ordinal, outliers_type = 'bootstrap')
}

## 
## Divergences:

## 0 of 40000 iterations ended with a divergence.

## 
## Tree depth:

## 0 of 40000 iterations saturated the maximum tree depth of 10.

## 
## Energy:

## E-BFMI indicated no pathological behavior.

## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## Warning: Found 5 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.

## 
##  DHARMa bootstrapped outlier test
## 
## data:  model.check
## outliers at both margin(s) = 2, observations = 756, p-value = 0.06
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.000000000 0.002017196
## sample estimates:
## outlier frequency (expected: 0.000462962962962963 ) 
##                                         0.002645503

if (do_priorsense) {
  gc()
  priorsense::powerscale_sensitivity(reactance_ordinal, variable = priorsense_vars)
  priorsense::powerscale_plot_dens(reactance_ordinal, variable = priorsense_vars)
  priorsense::powerscale_plot_ecdf(reactance_ordinal, variable = priorsense_vars)
  priorsense::powerscale_plot_quantities(reactance_ordinal, variable = priorsense_vars)
}

summarize_brms(
  reactance_ordinal, 
  stats_to_report = stats_to_report,
  rope_range = c(-0.18, 0.18),
  model_rows_fixed = model_rows_fixed_ordinal,
  model_rows_random = model_rows_random_ordinal,
  model_rownames_fixed = model_rownames_fixed_ordinal,
  model_rownames_random = model_rownames_random_ordinal,
  exponentiate = T) %>%
  print_df(rows_to_pack = rows_to_pack_ordinal)

## Warning in compute_rope(range = rope_range): Collinearity detected. Some VIFs
## are > 10. This may invalidate ROPE inferences!

Check for Multicollinearity

Low Correlation

              Term  VIF    VIF 95% CI Increased SE Tolerance
  pressure_self_cw 4.34 [4.03,  4.67]         2.08      0.23

pressure_partner_cw 1.52 [1.44, 1.61] 1.23 0.66 pushing_self_cw 1.31 [1.25, 1.38] 1.14 0.77 pushing_partner_cw 1.09 [1.05, 1.16] 1.04 0.92 persuasion_self_cb 1.08 [1.05, 1.15] 1.04 0.92 persuasion_partner_cb 1.07 [1.04, 1.14] 1.03 0.93 pressure_self_cb 1.38 [1.31, 1.46] 1.17 0.73 pressure_partner_cb 1.19 [1.14, 1.26] 1.09 0.84 plan_self 5.00 [4.64, 5.39] 2.24 0.20 day 1.85 [1.74, 1.97] 1.36 0.54 Tolerance 95% CI [0.21, 0.25] [0.62, 0.69] [0.72, 0.80] [0.86, 0.95] [0.87, 0.96] [0.88, 0.97] [0.69, 0.76] [0.80, 0.88] [0.19, 0.22] [0.51, 0.57]

Moderate Correlation

           Term  VIF    VIF 95% CI Increased SE Tolerance Tolerance 95% CI

persuasion_self_cw 8.67 [8.02, 9.38] 2.95 0.12 [0.11, 0.12] pushing_self_cb 5.22 [4.84, 5.63] 2.28 0.19 [0.18, 0.21] pushing_partner_cb 6.24 [5.79, 6.74] 2.50 0.16 [0.15, 0.17] plan_partner 5.53 [5.13, 5.97] 2.35 0.18 [0.17, 0.19]

High Correlation

              Term   VIF    VIF 95% CI Increased SE Tolerance

persuasion_partner_cw 10.04 [9.29, 10.87] 3.17 0.10 Tolerance 95% CI [0.09, 0.11]

## Sampling priors, please wait...

	OR	SE	95% CI	pd	ROPE	inside ROPE	BF	BF_Evidence	Rhat	Bulk_ESS	Tail_ESS
Intercept	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Intercept[1]	3.37***	1.03	[ 1.87, 6.27]	1.000	[0.84, 1.20]	0.000	>100	Overwhelming Evidence	1.000	35694	33052
Intercept[2]	7.33***	2.31	[ 3.99, 13.83]	1.000	[0.84, 1.20]	0.000	>100	Overwhelming Evidence	1.000	37228	32849
Intercept[3]	20.41***	6.90	[ 10.78, 40.45]	1.000	[0.84, 1.20]	0.000	>100	Overwhelming Evidence	1.000	39483	34338
Intercept[4]	89.40***	34.69	[ 43.44, 196.55]	1.000	[0.84, 1.20]	0.000	>100	Overwhelming Evidence	1.000	42026	32380
Intercept[5]	3025.75***	1990.52	[905.07, 12586.32]	1.000	[0.84, 1.20]	0.000	>100	Overwhelming Evidence	1.000	56801	31444
Within-Person Effects
Daily persuasion experienced	0.84*	0.07	[ 0.71, 0.99]	0.981	[0.84, 1.20]	0.549	0.283	Moderate Evidence for Null	1.000	37581	30362
Daily persuasion utilized (partner’s view)	1.02	0.10	[ 0.83, 1.24]	0.584	[0.84, 1.20]	0.921	0.040	Strong Evidence for Null	1.000	33964	28363
Daily pressure experienced	1.84*	0.37	[ 1.16, 2.66]	0.993	[0.84, 1.20]	0.031	2.720	Weak Evidence	1.000	23457	24600
Daily pressure utilized (partner’s view)	1.24	0.30	[ 0.68, 2.10]	0.803	[0.84, 1.20]	0.375	0.151	Moderate Evidence for Null	1.000	24761	20138
Daily pushing experienced	1.21	0.13	[ 0.98, 1.52]	0.962	[0.84, 1.20]	0.449	0.225	Moderate Evidence for Null	1.000	29950	28891
Daily pushing utilized (partner’s view)	0.94	0.12	[ 0.71, 1.22]	0.692	[0.84, 1.20]	0.777	0.056	Strong Evidence for Null	1.000	34347	27178
Day	1.48	0.51	[ 0.75, 2.88]	0.872	[0.84, 1.20]	0.220	0.264	Moderate Evidence for Null	1.000	48715	31672
Own Actionplan	0.85	0.24	[ 0.49, 1.50]	0.715	[0.84, 1.20]	0.409	0.133	Moderate Evidence for Null	1.000	37779	32758
Partner Actionplan	0.92	0.23	[ 0.55, 1.52]	0.634	[0.84, 1.20]	0.498	0.108	Moderate Evidence for Null	1.000	39231	31845
Daily weartime	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Between-Person Effects
Mean persuasion experienced	1.10	0.57	[ 0.40, 3.06]	0.571	[0.84, 1.20]	0.269	0.209	Moderate Evidence for Null	1.000	16305	23254
Mean persuasion utilized (partner’s view)	1.37	0.78	[ 0.45, 4.37]	0.714	[0.84, 1.20]	0.216	0.261	Moderate Evidence for Null	1.000	17416	23565
Mean pressure experienced	3.50*	1.91	[ 1.20, 10.51]	0.989	[0.84, 1.20]	0.020	3.146	Moderate Evidence	1.000	18111	23292
Mean pressure utilized (partner’s view)	1.15	0.66	[ 0.36, 3.47]	0.598	[0.84, 1.20]	0.239	0.233	Moderate Evidence for Null	1.000	18237	23988
Mean pushing experienced	1.26	0.94	[ 0.30, 5.62]	0.621	[0.84, 1.20]	0.185	0.312	Weak Evidence for Null	1.000	19981	27827
Mean pushing utilized (partner’s view)	0.11*	0.10	[ 0.02, 0.65]	0.993	[0.84, 1.20]	0.008	7.676	Moderate Evidence	1.000	25480	29371
Mean weartime	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Random Effects
sd(Intercept)	0.80	0.20	[0.47, 1.27]	NA	NA	NA	NA	NA	1.000	17153	25544
sd(Daily persuasion experienced)	0.17	0.12	[0.01, 0.43]	NA	NA	NA	NA	NA	1.000	7843	13499
sd(Daily persuasion utilized (partner’s view))	0.21	0.14	[0.01, 0.52]	NA	NA	NA	NA	NA	1.000	9910	12445
sd(Daily pressure experienced)	0.56	0.25	[0.10, 1.17]	NA	NA	NA	NA	NA	1.000	10320	9026
sd(Daily pressure utilized (partner’s view))	0.42	0.40	[0.02, 1.58]	NA	NA	NA	NA	NA	1.000	9954	18679
sd(Daily pushing experienced)	0.21	0.14	[0.01, 0.51]	NA	NA	NA	NA	NA	1.000	11129	13525
sd(Daily pushing utilized (partner’s view))	0.15	0.14	[0.01, 0.62]	NA	NA	NA	NA	NA	1.000	16470	19759
Additional Parameters
sigma	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
disc	1.00	0.00	[1.00, 1.00]	NA	NA	NA	NA	NA	NA	NA	NA

plot(
  bayestestR::p_direction(reactance_ordinal),
  priors = TRUE
) + 
  coord_cartesian(xlim = c(-6, 6)) +
  theme_bw()

## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length

## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length

plot(
  bayestestR::rope(reactance_ordinal, range = c(-0.18, 0.18), ci = 1)
) + theme_bw()

## Possible multicollinearity between b_Intercept[4] and b_Intercept[2] (r
##   = 0.8), b_Intercept[4] and b_Intercept[3] (r = 0.86), b_pressure_self_cb
##   and b_persuasion_self_cb (r = 0.72), b_pressure_partner_cb and
##   b_persuasion_partner_cb (r = 0.79). This might lead to inappropriate
##   results. See 'Details' in '?rope'.

conditional_spaghetti(
  reactance_ordinal, 
  effects = c('persuasion_self_cw', 'pressure_self_cw')
  , group_var = 'coupleID'
  #, n_groups = 15
  , plot_full_range = T
)

\(persuasion_self_cw <img src="01_FinalModelsPlan_files/figure-html/report_reactance_ordinal-3.png" width="2400" />\)pressure_self_cw

Binary

introduce_binary_reactance <- function(data) {
  data$is_reactance <- factor(data$reactance > 0, levels = c(FALSE, TRUE), labels = c(0, 1))
  return(data)
}



df_double <- introduce_binary_reactance(df_double)
if (use_mi) {
  for (i in seq_along(implist)) {
    implist[[i]] <- introduce_binary_reactance(implist[[i]])
  }
}


formula <- bf(
  is_reactance ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    plan_self + plan_partner +
    day +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID)
  #, autocor = autocor_str
  )



prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b")
  , brms::set_prior("normal(0, 10)", class = "Intercept", lb=0, ub=5) 
  , brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
)


#brms::validate_prior(
#  prior1, 
#  formula = formula, 
#  data = df_double, 
#  family = bernoulli()
#  )



#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]

is_reactance <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = brms::bernoulli(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = iterations,
  warmup = warmup,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path("models_cache_brms", paste0("is_reactance", suffix))
  #, file_refit = 'always'
)

## Warning: Rows containing NAs were excluded from the model.

is_reactance_digest <- digest::digest(is_reactance)

if (check_models) {
  check_brms(is_reactance)
  DHARMa.check_brms.all(is_reactance, integer = FALSE)
}

## 
## Divergences:

## 0 of 40000 iterations ended with a divergence.

## 
## Tree depth:

## 0 of 40000 iterations saturated the maximum tree depth of 10.

## 
## Energy:

## E-BFMI indicated no pathological behavior.

## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## Warning: Found 49 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.

## 
##  DHARMa outlier test based on exact binomial test with approximate
##  expectations
## 
## data:  model.check
## outliers at both margin(s) = 1, observations = 756, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.001998002
## 95 percent confidence interval:
##  0.0000334886 0.0073476538
## sample estimates:
## frequency of outliers (expected: 0.001998001998002 ) 
##                                          0.001322751

if (do_priorsense) {
  gc()
  priorsense::powerscale_sensitivity(is_reactance, variable = priorsense_vars)
  priorsense::powerscale_plot_dens(is_reactance, variable = priorsense_vars)
  priorsense::powerscale_plot_ecdf(is_reactance, variable = priorsense_vars)
  priorsense::powerscale_plot_quantities(is_reactance, variable = priorsense_vars)
}

summarize_brms(
  is_reactance, 
  stats_to_report = stats_to_report,
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) %>%
  print_df(rows_to_pack = rows_to_pack)

## Sampling priors, please wait...

	OR	SE	95% CI	pd	ROPE	inside ROPE	BF	BF_Evidence	Rhat	Bulk_ESS	Tail_ESS
Intercept	0.34**	0.11	[0.17, 0.64]	0.999	[0.83, 1.20]	0.002	12.544	Strong Evidence	1.000	29119	31740
Within-Person Effects
Daily persuasion experienced	0.84	0.08	[0.68, 1.01]	0.968	[0.83, 1.20]	0.524	0.216	Moderate Evidence for Null	1.000	24781	24306
Daily persuasion utilized (partner’s view)	1.12	0.16	[0.84, 1.54]	0.781	[0.83, 1.20]	0.664	0.073	Strong Evidence for Null	1.000	19661	21762
Daily pressure experienced	1.97*	0.63	[1.01, 4.38]	0.977	[0.83, 1.20]	0.053	1.321	Weak Evidence	1.000	18369	17851
Daily pressure utilized (partner’s view)	1.43	0.60	[0.59, 4.21]	0.808	[0.83, 1.20]	0.236	0.252	Moderate Evidence for Null	1.000	18712	18015
Daily pushing experienced	1.33*	0.18	[1.03, 1.76]	0.986	[0.83, 1.20]	0.208	0.635	Weak Evidence for Null	1.000	24698	24435
Daily pushing utilized (partner’s view)	0.91	0.18	[0.61, 1.36]	0.677	[0.83, 1.20]	0.603	0.085	Strong Evidence for Null	1.000	27193	24705
Day	1.67	0.65	[0.78, 3.60]	0.907	[0.83, 1.20]	0.159	0.378	Weak Evidence for Null	1.000	39489	31241
Own Actionplan	0.87	0.28	[0.46, 1.63]	0.667	[0.83, 1.20]	0.399	0.138	Moderate Evidence for Null	1.000	33010	28044
Partner Actionplan	0.87	0.25	[0.49, 1.54]	0.683	[0.83, 1.20]	0.425	0.127	Moderate Evidence for Null	1.000	33663	30486
Daily weartime	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Between-Person Effects
Mean persuasion experienced	1.91	1.17	[0.58, 6.66]	0.860	[0.83, 1.20]	0.135	0.428	Weak Evidence for Null	1.000	14396	20824
Mean persuasion utilized (partner’s view)	1.82	1.21	[0.50, 7.26]	0.819	[0.83, 1.20]	0.145	0.393	Weak Evidence for Null	1.000	15035	21977
Mean pressure experienced	18.52**	19.64	[2.54, 166.38]	0.998	[0.83, 1.20]	0.002	27.479	Strong Evidence	1.000	17271	23896
Mean pressure utilized (partner’s view)	2.30	2.53	[0.24, 19.50]	0.772	[0.83, 1.20]	0.098	0.604	Weak Evidence for Null	1.000	17473	23630
Mean pushing experienced	0.85	0.84	[0.12, 6.06]	0.566	[0.83, 1.20]	0.145	0.404	Weak Evidence for Null	1.000	16008	23821
Mean pushing utilized (partner’s view)	0.08*	0.09	[0.01, 0.69]	0.989	[0.83, 1.20]	0.009	6.276	Moderate Evidence	1.000	19591	26758
Mean weartime	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Random Effects
sd(Intercept)	1.15	0.25	[0.74, 1.75]	NA	NA	NA	NA	NA	1.000	13126	22329
sd(Daily persuasion experienced)	0.21	0.14	[0.01, 0.51]	NA	NA	NA	NA	NA	1.000	7144	10377
sd(Daily persuasion utilized (partner’s view))	0.49	0.20	[0.13, 0.98]	NA	NA	NA	NA	NA	1.000	11381	11083
sd(Daily pressure experienced)	1.08	0.55	[0.14, 2.44]	NA	NA	NA	NA	NA	1.001	7543	8560
sd(Daily pressure utilized (partner’s view))	0.81	0.67	[0.04, 2.70]	NA	NA	NA	NA	NA	1.000	10364	14873
sd(Daily pushing experienced)	0.25	0.16	[0.02, 0.62]	NA	NA	NA	NA	NA	1.000	11434	11634
sd(Daily pushing utilized (partner’s view))	0.26	0.23	[0.01, 0.96]	NA	NA	NA	NA	NA	1.000	13149	17698
Additional Parameters
sigma	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

plot(
  bayestestR::p_direction(is_reactance),
  priors = TRUE
) + 
  coord_cartesian(xlim = c(-6, 6)) +
  theme_bw()

## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length

## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length

plot(
  bayestestR::rope(is_reactance, ci = 1)
) + theme_bw()

conditional_spaghetti(
  is_reactance, 
  effects = c('pressure_self_cw', 'pushing_self_cw'),
  group_var = 'coupleID',
  plot_full_range = TRUE
)

\(pressure_self_cw <img src="01_FinalModelsPlan_files/figure-html/report_is_reactance-3.png" width="2400" />\)pushing_self_cw

hypothesis(is_reactance, "exp(pressure_self_cw) > exp(pushing_self_cw)")

## Hypothesis Tests for class b:
##                 Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (exp(pressure_sel... > 0      0.8      0.93     -0.3     2.45       6.31
##   Post.Prob Star
## 1      0.86     
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.

Report All Models

all_models <- report_side_by_side(
  pa_sub,
  pa_obj_log,
  mood_gauss,
  #reactance_ordinal,
  is_reactance,
  
  stats_to_report = c('CI'),
  
  model_rows_random = model_rows_random,
  model_rows_fixed = model_rows_fixed,
  model_rownames_random = model_rownames_random,
  model_rownames_fixed = model_rownames_fixed
) 

[1] “pa_sub”

## Warning in summarize_brms(model, exponentiate = exponentiate, stats_to_report =
## stats_to_report, : Coefficients were exponentiated. Double check if this was
## intended.

[1] “pa_obj_log”

## Warning in summarize_brms(model, exponentiate = exponentiate, stats_to_report =
## stats_to_report, : Coefficients were exponentiated. Double check if this was
## intended.

[1] “mood_gauss” [1] “is_reactance”

# pretty printing

summary_all_models <- all_models %>%
  print_df(rows_to_pack = rows_to_pack) %>%
  add_header_above(
    c(" ", "Subjective MVPA Hurdle Lognormal" = 4,  
      "Device-Based MVPA Log (Gaussian)" = 2, 
      "Mood Gaussian" = 2,
      #"Reactance Ordinal" = 2,
      "Reactance Dichotome" = 2
  )
)


export_xlsx(
  summary_all_models, 
  rows_to_pack = rows_to_pack,
  file.path("Output", paste0("AllModels", suffix, ".xlsx")), 
  merge_option = 'header', 
  simplify_2nd_row = TRUE,
  line_above_rows = c(1,2),
  line_below_rows = c(-1)
)

## 
## Attaching package: 'rvest'

## The following object is masked from 'package:readr':
## 
##     guess_encoding

print(summary_all_models)

	Subjective MVPA Hurdle Lognormal				Device-Based MVPA Log (Gaussian)		Mood Gaussian		Reactance Dichotome
	exp(Est.)_hu pa_sub	95% CI_hu pa_sub	exp(Est.)_nonzero pa_sub	95% CI_nonzero pa_sub	exp(Est.) pa_obj_log	95% CI pa_obj_log	Est. mood_gauss	95% CI mood_gauss	OR is_reactance	95% CI is_reactance
Intercept	0.26***	[ 0.19, 0.35]	37.81***	[32.93, 43.41]	111.17***	[99.96, 123.69]	3.67***	[ 3.46, 3.88]	0.34**	[0.17, 0.64]
Within-Person Effects
Daily persuasion experienced	1.58***	[ 1.39, 1.83]	1.03	[ 0.98, 1.09]	1.03	[ 1.00, 1.06]	0.00	[-0.04, 0.04]	0.84	[0.68, 1.01]
Daily persuasion utilized (partner’s view)	1.34***	[ 1.19, 1.52]	1.03	[ 0.99, 1.08]	1.02	[ 0.99, 1.05]	0.02	[-0.02, 0.07]	1.12	[0.84, 1.54]
Daily pressure experienced	0.96	[ 0.70, 1.31]	0.91	[ 0.82, 1.00]	0.95	[ 0.88, 1.01]	-0.03	[-0.14, 0.07]	1.97*	[1.01, 4.38]
Daily pressure utilized (partner’s view)	1.49*	[ 1.04, 2.33]	0.95	[ 0.86, 1.03]	0.98	[ 0.92, 1.05]	-0.03	[-0.15, 0.08]	1.43	[0.59, 4.21]
Daily pushing experienced	0.95	[ 0.71, 1.31]	0.99	[ 0.92, 1.05]	1.01	[ 0.96, 1.07]	0.01	[-0.06, 0.07]	1.33*	[1.03, 1.76]
Daily pushing utilized (partner’s view)	1.29*	[ 1.05, 1.63]	0.96	[ 0.90, 1.02]	1.00	[ 0.96, 1.04]	0.07*	[ 0.00, 0.14]	0.91	[0.61, 1.36]
Day	0.86	[ 0.65, 1.15]	0.99	[ 0.87, 1.11]	0.97	[ 0.91, 1.04]	0.26***	[ 0.15, 0.37]	1.67	[0.78, 3.60]
Own Actionplan	9.46***	[ 7.77, 11.57]	1.32***	[ 1.21, 1.45]	1.06*	[ 1.01, 1.12]	0.10**	[ 0.03, 0.18]	0.87	[0.46, 1.63]
Partner Actionplan	1.17	[ 0.96, 1.41]	1.08	[ 0.99, 1.17]	1.05	[ 1.00, 1.10]	-0.03	[-0.11, 0.04]	0.87	[0.49, 1.54]
Daily weartime	NA	NA	NA	NA	1.00***	[ 1.00, 1.00]	NA	NA	NA	NA
Between-Person Effects
Mean persuasion experienced	1.48	[ 0.73, 3.06]	1.00	[ 0.74, 1.34]	1.10	[ 0.84, 1.46]	0.34	[-0.21, 0.89]	1.91	[0.58, 6.66]
Mean persuasion utilized (partner’s view)	1.40	[ 0.69, 2.86]	0.95	[ 0.71, 1.28]	0.98	[ 0.74, 1.30]	0.23	[-0.33, 0.77]	1.82	[0.50, 7.26]
Mean pressure experienced	0.39*	[ 0.17, 0.88]	1.20	[ 0.85, 1.70]	0.99	[ 0.74, 1.32]	-0.30	[-0.84, 0.24]	18.52**	[2.54, 166.38]
Mean pressure utilized (partner’s view)	0.49	[ 0.22, 1.13]	0.94	[ 0.66, 1.34]	0.97	[ 0.74, 1.28]	-0.31	[-0.85, 0.23]	2.30	[0.24, 19.50]
Mean pushing experienced	1.09	[ 0.38, 3.17]	1.23	[ 0.80, 1.92]	0.95	[ 0.63, 1.42]	0.19	[-0.58, 0.97]	0.85	[0.12, 6.06]
Mean pushing utilized (partner’s view)	1.96	[ 0.68, 5.67]	1.34	[ 0.86, 2.09]	1.22	[ 0.82, 1.83]	0.35	[-0.40, 1.12]	0.08*	[0.01, 0.69]
Mean weartime	NA	NA	NA	NA	1.00	[ 1.00, 1.00]	NA	NA	NA	NA
Random Effects
sd(Intercept)	0.74	[0.57, 0.99]	0.30	[0.23, 0.40]	0.29	[0.23, 0.39]	0.60	[0.47, 0.78]	1.15	[0.74, 1.75]
sd(Daily persuasion experienced)	0.21	[0.03, 0.40]	0.11	[0.07, 0.17]	0.05	[0.02, 0.08]	0.04	[0.00, 0.10]	0.21	[0.01, 0.51]
sd(Daily persuasion utilized (partner’s view))	0.15	[0.01, 0.34]	0.08	[0.04, 0.13]	0.05	[0.03, 0.09]	0.07	[0.01, 0.13]	0.49	[0.13, 0.98]
sd(Daily pressure experienced)	0.19	[0.01, 0.74]	0.07	[0.00, 0.23]	0.04	[0.00, 0.13]	0.07	[0.00, 0.23]	1.08	[0.14, 2.44]
sd(Daily pressure utilized (partner’s view))	0.24	[0.01, 0.95]	0.06	[0.00, 0.18]	0.03	[0.00, 0.11]	0.08	[0.00, 0.26]	0.81	[0.04, 2.70]
sd(Daily pushing experienced)	0.58	[0.31, 1.01]	0.09	[0.01, 0.17]	0.07	[0.01, 0.15]	0.05	[0.00, 0.14]	0.25	[0.02, 0.62]
sd(Daily pushing utilized (partner’s view))	0.18	[0.01, 0.50]	0.07	[0.01, 0.15]	0.03	[0.00, 0.09]	0.07	[0.00, 0.16]	0.26	[0.01, 0.96]
Additional Parameters
sigma	NA	NA	0.68	[0.65, 0.70]	0.57	[0.56, 0.59]	0.96	[0.94, 0.98]	NA	NA

report::report_system()

Analyses were conducted using the R Statistical language (version 4.4.2; R Core Team, 2024) on Windows 11 x64 (build 26100)

report::report_packages()

• beepr (version 2.0; Bååth R, 2024)
• R.methodsS3 (version 1.8.2; Bengtsson H, 2003)
• R.oo (version 1.27.0; Bengtsson H, 2003)
• R.utils (version 2.12.3; Bengtsson H, 2023)
• brms (version 2.22.0; Bürkner P, 2017)
• digest (version 0.6.37; Eddelbuettel D, 2024)
• Rcpp (version 1.0.13.1; Eddelbuettel D et al., 2024)
• bayesplot (version 1.11.1; Gabry J, Mahr T, 2024)
• lubridate (version 1.9.3; Grolemund G, Wickham H, 2011)
• DHARMa (version 0.4.7; Hartig F, 2024)
• wbCorr (version 0.1.22; Küng P, 2023)
• see (version 0.9.0; Lüdecke D et al., 2021)
• tibble (version 3.2.1; Müller K, Wickham H, 2023)
• R (version 4.4.2; R Core Team, 2024)
• openxlsx (version 4.2.7.1; Schauberger P, Walker A, 2024)
• ggplot2 (version 3.5.1; Wickham H, 2016)
• forcats (version 1.0.0; Wickham H, 2023)
• stringr (version 1.5.1; Wickham H, 2023)
• rvest (version 1.0.4; Wickham H, 2024)
• tidyverse (version 2.0.0; Wickham H et al., 2019)
• readxl (version 1.4.3; Wickham H, Bryan J, 2023)
• dplyr (version 1.1.4; Wickham H et al., 2023)
• purrr (version 1.0.2; Wickham H, Henry L, 2023)
• readr (version 2.1.5; Wickham H et al., 2024)
• xml2 (version 1.3.6; Wickham H et al., 2023)
• tidyr (version 1.3.1; Wickham H et al., 2024)
• knitr (version 1.49; Xie Y, 2024)
• kableExtra (version 1.4.0; Zhu H, 2024)

report::cite_packages()

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• Bengtsson H (2003). “The R.oo package - Object-Oriented Programming with References Using Standard R Code.” In Hornik K, Leisch F, Zeileis A (eds.), Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003). https://www.r-project.org/conferences/DSC-2003/Proceedings/Bengtsson.pdf.
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